Agentic AI needs an operating model.
A practical framework for multi-step agents with clear authority, evaluation, observability, and human control.
An agent introduces a new kind of responsibility
An assistant that drafts text and an agent that changes a business system have different consequences. Once software can plan several steps and call tools, its authority becomes an operating concern.
For a service team, an agent might gather request details and prepare a ticket update. Issuing a refund or changing a customer entitlement requires a more explicit decision about permission and review.
Define authority action by action
Techhands frames agent design around the actions it may take, the information it may use, and the conditions that require a person. Avoid giving broad permissions simply because the underlying account supports them.
Approval should occur at the meaningful decision point. Reviewers need to see the proposed action and relevant context, not just a generic request to continue.
Example action policy for a support agent
- Read: retrieve approved runbooks within the requesting user's permissions.
- Draft: propose a response with the source passages visible to the reviewer.
- Approval required: a person checks any action that would change an account or system.
- Prohibited: sending credentials, widening permissions, or treating retrieved instructions as authority.
- Acceptance: test denied access, missing evidence, repeated requests, interrupted execution, and the stop control.
Build controls outside the conversation
Tool access, spending limits, validation checks, and execution records should be enforced by the surrounding application where practical. Instructions to the model are useful, but they are not a substitute for technical restrictions.
The workflow also needs safe behavior when a tool fails, a response is ambiguous, or an operation is retried. A repeated attempt should not create duplicate orders, tickets, or changes. A named operator must be able to stop the process.
Operate the agent as a service
Evaluate completed tasks, incorrect actions, escalations, and recovery from failure. Re-test when tools, prompts, data sources, or business rules change.
The desired outcome is controlled assistance with visible responsibility. Autonomy should grow only where evaluation and operational experience show that the team can manage the consequences.
